About
Highly motivated Computer Science and Engineering student with a strong foundation in AI/Machine Learning, Deep Learning, and Full-Stack Development. Proven ability to build and deploy innovative solutions, including AI-powered financial platforms and NLP systems, leveraging advanced frameworks and cloud technologies. Seeking to apply robust technical skills and a passion for problem-solving in challenging AI/ML or full-stack engineering roles.
Work
→
Summary
Contributed to Chronocept, an AI research initiative focused on enhancing machine temporal reasoning by integrating temporal validity into Natural Language Processing (NLP) systems.
Highlights
Annotated over 250 text samples using a structured three-step process (Text Segmentation, Temporal Axis Classification, Temporal Validity Modeling) to enhance temporal reasoning in NLP systems.
Co-developed the Chronocept Dataset, a benchmark dataset specifically designed to advance AI-driven temporal reasoning capabilities in NLP models.
Aided in distinguishing between past, present, and future occurrences with greater accuracy for AI models.
Education
→
B.Tech
Computer Science and Engineering
Grade: 8.66/10 CGPA
Courses
Machine Learning
Deep Learning
Data Structures and Algorithms
Data Science
Artificial Intelligence
Statistics and Probability
Computer Organization and Architecture
Advanced Mathematics for Data Science
Linear Algebra for ML
Skills
Programming Languages
Python, C, C++, Java, JavaScript, TypeScript, SQL.
Technologies & Frameworks
React, Node.js, Express.js, FastAPI, Flask, Docker, MLflow, ZenML, Tensorflow, Keras, Hugging Face Transformers, Scikit-learn, LangGraph, LangChain, Jupyter Notebook, Colab, Git, GitHub, VS Code.
Databases
MongoDB, PostgreSQL, Prisma.
Cloud & Platforms
Cloudflare Workers, AWS, Vercel.
Domains & Expertise
Full-Stack Development, Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Computer Vision (CV), Data Analysis.